Large-scale filtering
Data used to create these indices were taken from the GSMFC website in March of 2019. Data was then limited to 40 ft trawls, and any tows with no operation codes (indicating a bad station) or an op code of “W” (water tow, aka no catch but otherwise fine). Data was also limited to summer and fall groundfish cruises specifically. Stations with no Stat zone or tow speed records were also removed.
A survey design change ocurred in 2008 between the summer and fall groundfish surveys. This change expanded the area sampling into Florida waters, an area previously unsampled, and changed from a dapth stratified sampling design where multiple tows longer than 30min were common. The new (current) sampling design covers the entire Gulf from Brownsville TX to the Florida Keyes, and each station consists of a single 30 minute tow.
It was recommended to me by the analyst group at the Pascagoula Laboratory to use groundfish data from 2010* forward for consistency as the design change was not uniformly applied until then, with some state partners sampling differently.
Based on exploration of the data, and in the interest of looking at Gulfwide or atleast East/West trends several additional filters were added. Data was limited to years after 2009 for consistency in the sampling methods and for a Gulfwide coverage. Additionally statzones 0 (not a real zone), 1 (The Keyes), 12 (MS Sound, inside Chandy), and 22 (Brownsville/MX) were not included due to a lack of observations. Stat zones 6 & 9 were also excluded from the model, as they have no observed C. sapidus catch for the timeseries and thus have no expected catch.
There was an initial concern with abnormally high catch values being potential mis-identifications. This is less of a concern with the reduced time-series
Total Number of Stations = 6254
Starting in 2009 and continuing through 2018
Total Sapidus Caught was 2280
Overall Frequency of Occurrence was 13.4793732
Overall Mean Catch: 0.3645667 Stations with no catch: 5411 / 6254 or 86.5206268
| Survey_Year | Stations | Missing Obs | % Occurrence | Mean Catch | SD | Overdispersed |
|---|---|---|---|---|---|---|
| 2009 | 983 | 0 | 0.1576806 | 0.3601221 | 1.2885667 | yes |
| 2010 | 700 | 0 | 0.1571429 | 0.4271429 | 1.5527373 | yes |
| 2011 | 549 | 0 | 0.2021858 | 0.6484517 | 2.2777691 | yes |
| 2012 | 589 | 0 | 0.0916808 | 0.1782683 | 0.7050376 | yes |
| 2013 | 506 | 0 | 0.0790514 | 0.1620553 | 0.8309740 | yes |
| 2014 | 685 | 0 | 0.1138686 | 0.3795620 | 3.7065837 | yes |
| 2015 | 700 | 0 | 0.1314286 | 0.3728571 | 1.7779786 | yes |
| 2016 | 548 | 0 | 0.1423358 | 0.3521898 | 1.3541374 | yes |
| 2017 | 603 | 0 | 0.1293532 | 0.4560531 | 1.8992853 | yes |
| 2018 | 391 | 0 | 0.1202046 | 0.2429668 | 1.0050107 | yes |
| StatZone | Stations | Missing Obs | % Occurrence | Mean Catch | SD | Overdispersed |
|---|---|---|---|---|---|---|
| 1 | 5 | 0 | 0.0000000 | 0.0000000 | 0.0000000 | no |
| 2 | 125 | 0 | 0.0160000 | 0.0160000 | 0.1259800 | no |
| 3 | 399 | 0 | 0.0100251 | 0.0100251 | 0.0997472 | no |
| 4 | 443 | 0 | 0.0067720 | 0.0135440 | 0.1774552 | yes |
| 5 | 396 | 0 | 0.0075758 | 0.0101010 | 0.1228315 | yes |
| 6 | 395 | 0 | 0.0000000 | 0.0000000 | 0.0000000 | no |
| 7 | 245 | 0 | 0.0122449 | 0.0163265 | 0.1559568 | yes |
| 8 | 268 | 0 | 0.0559701 | 0.2126866 | 1.4047497 | yes |
| 9 | 191 | 0 | 0.0000000 | 0.0000000 | 0.0000000 | no |
| 10 | 173 | 0 | 0.0462428 | 0.0462428 | 0.2106200 | no |
| 11 | 366 | 0 | 0.2349727 | 0.5901639 | 1.7253347 | yes |
| 12 | 17 | 0 | 0.1764706 | 0.4117647 | 1.0641207 | yes |
| 13 | 129 | 0 | 0.3875969 | 1.3333333 | 3.6407703 | yes |
| 14 | 285 | 0 | 0.2491228 | 0.5578947 | 1.4465777 | yes |
| 15 | 339 | 0 | 0.2684366 | 0.5988201 | 1.6636023 | yes |
| 16 | 440 | 0 | 0.2431818 | 0.7022727 | 2.1871429 | yes |
| 17 | 549 | 0 | 0.2386157 | 0.6885246 | 2.0216609 | yes |
| 18 | 506 | 0 | 0.2055336 | 0.5474308 | 1.9280944 | yes |
| 19 | 353 | 0 | 0.2322946 | 0.8441926 | 5.1902527 | yes |
| 20 | 361 | 0 | 0.1274238 | 0.2908587 | 1.2610179 | yes |
| 21 | 268 | 0 | 0.1268657 | 0.2649254 | 0.9163318 | yes |
| region | Stations | Missing Obs | % Occurrence | Mean Catch | SD | Overdispersed |
|---|---|---|---|---|---|---|
| Texas | 1488 | 0 | 0.1787634 | 0.5047043 | 2.8678548 | yes |
| Louisiana | 1743 | 0 | 0.2581756 | 0.7005164 | 2.0925308 | yes |
| MS Bight | 556 | 0 | 0.1744604 | 0.4154676 | 1.4376075 | yes |
| Florida | 2467 | 0 | 0.0121605 | 0.0312120 | 0.4801817 | yes |
| region | Season | Stations | Missing Obs | % Occurrence | Mean Catch | SD | Overdispersed |
|---|---|---|---|---|---|---|---|
| Texas | Summer | 827 | 0 | 0.2394196 | 0.7255139 | 3.6214139 | yes |
| Texas | Fall | 661 | 0 | 0.1028744 | 0.2284418 | 1.4069661 | yes |
| Louisiana | Summer | 977 | 0 | 0.3387922 | 0.9713408 | 2.3900403 | yes |
| Louisiana | Fall | 766 | 0 | 0.1553525 | 0.3550914 | 1.5715673 | yes |
| MS Bight | Summer | 315 | 0 | 0.2444444 | 0.6571429 | 1.8502637 | yes |
| MS Bight | Fall | 241 | 0 | 0.0829876 | 0.0995851 | 0.3512475 | yes |
| Florida | Summer | 1540 | 0 | 0.0162338 | 0.0467532 | 0.6046354 | yes |
| Florida | Fall | 927 | 0 | 0.0053937 | 0.0053937 | 0.0732833 | no |
| Survey_Year | Season | Stations | Missing Obs | % Occurrence | Mean Catch | SD | Overdispersed |
|---|---|---|---|---|---|---|---|
| 2009 | Summer | 539 | 0 | 0.2393321 | 0.5918367 | 1.6765406 | yes |
| 2009 | Fall | 444 | 0 | 0.0585586 | 0.0788288 | 0.3499145 | yes |
| 2010 | Summer | 386 | 0 | 0.2020725 | 0.6217617 | 1.9611666 | yes |
| 2010 | Fall | 314 | 0 | 0.1019108 | 0.1878981 | 0.7411114 | yes |
| 2011 | Summer | 329 | 0 | 0.2401216 | 0.7294833 | 1.9637911 | yes |
| 2011 | Fall | 220 | 0 | 0.1454545 | 0.5272727 | 2.6799069 | yes |
| 2012 | Summer | 391 | 0 | 0.1023018 | 0.2020460 | 0.7179714 | yes |
| 2012 | Fall | 198 | 0 | 0.0707071 | 0.1313131 | 0.6781052 | yes |
| 2013 | Summer | 313 | 0 | 0.1118211 | 0.2300319 | 0.9894276 | yes |
| 2013 | Fall | 193 | 0 | 0.0259067 | 0.0518135 | 0.4534696 | yes |
| 2014 | Summer | 364 | 0 | 0.1236264 | 0.5274725 | 5.0164263 | yes |
| 2014 | Fall | 321 | 0 | 0.1028037 | 0.2118380 | 0.8760619 | yes |
| 2015 | Summer | 377 | 0 | 0.1803714 | 0.5172414 | 1.8794365 | yes |
| 2015 | Fall | 323 | 0 | 0.0743034 | 0.2043344 | 1.6385438 | yes |
| 2016 | Summer | 354 | 0 | 0.1638418 | 0.4435028 | 1.5584641 | yes |
| 2016 | Fall | 194 | 0 | 0.1030928 | 0.1855670 | 0.8435049 | yes |
| 2017 | Summer | 322 | 0 | 0.1987578 | 0.7950311 | 2.5286348 | yes |
| 2017 | Fall | 281 | 0 | 0.0498221 | 0.0676157 | 0.3766170 | yes |
| 2018 | Summer | 284 | 0 | 0.1232394 | 0.2746479 | 1.1318977 | yes |
| 2018 | Fall | 107 | 0 | 0.1121495 | 0.1588785 | 0.5346403 | yes |
TableGrob (2 x 1) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (2-2,1-1) arrange gtable[layout]
TableGrob (2 x 1) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (2-2,1-1) arrange gtable[layout]
TableGrob (2 x 1) "arrange": 2 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (2-2,1-1) arrange gtable[layout]
TableGrob (3 x 2) "arrange": 6 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
3 3 (2-2,1-1) arrange gtable[layout]
4 4 (2-2,2-2) arrange gtable[layout]
5 5 (3-3,1-1) arrange gtable[layout]
6 6 (3-3,2-2) arrange gtable[layout]
TableGrob (2 x 3) "arrange": 6 grobs
z cells name grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
3 3 (1-1,3-3) arrange gtable[layout]
4 4 (2-2,1-1) arrange gtable[layout]
5 5 (2-2,2-2) arrange gtable[layout]
6 6 (2-2,3-3) arrange gtable[layout]
A negative binomial generalized linear model was used to estimate an index of abundance for the time series. The factors considered in the model were Year, Season, and Stat Zone.
Method : Move from the simplest model of catch and year to the most complex with year, season, statzone, depth, temp, and salinity.
Most parsimonious model (AIC) :
year + season + stat zone
OR
year + season + region
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -4.1399866 | 0.7335176 | -5.6440182 | 0.0000000 |
| Year_f2010 | 0.4622160 | 0.1654732 | 2.7932992 | 0.0052173 |
| Year_f2011 | 0.8056565 | 0.1664822 | 4.8392942 | 0.0000013 |
| Year_f2012 | -0.4183861 | 0.1939715 | -2.1569461 | 0.0310099 |
| Year_f2013 | -0.4808636 | 0.2086077 | -2.3051100 | 0.0211604 |
| Year_f2014 | 0.6173601 | 0.1710930 | 3.6083301 | 0.0003082 |
| Year_f2015 | 0.6073110 | 0.1699604 | 3.5732501 | 0.0003526 |
| Year_f2016 | 0.2421701 | 0.1801011 | 1.3446341 | 0.1787434 |
| Year_f2017 | 0.3667771 | 0.1771867 | 2.0700037 | 0.0384520 |
| Year_f2018 | 0.1321023 | 0.2257196 | 0.5852497 | 0.5583799 |
| SeasonFall | -1.2595060 | 0.0937274 | -13.4379691 | 0.0000000 |
| StatZone3 | -0.5327310 | 0.8882528 | -0.5997515 | 0.5486719 |
| StatZone4 | -0.1764824 | 0.8419341 | -0.2096154 | 0.8339678 |
| StatZone5 | -0.4477110 | 0.8907543 | -0.5026201 | 0.6152314 |
| StatZone7 | 0.1134053 | 0.8917659 | 0.1271694 | 0.8988063 |
| StatZone8 | 2.5723013 | 0.7534550 | 3.4140080 | 0.0006401 |
| StatZone10 | 1.2341286 | 0.8237717 | 1.4981441 | 0.1340958 |
| StatZone11 | 3.7437210 | 0.7388302 | 5.0670925 | 0.0000004 |
| StatZone13 | 4.7094645 | 0.7570346 | 6.2209371 | 0.0000000 |
| StatZone14 | 3.9054392 | 0.7417412 | 5.2652315 | 0.0000001 |
| StatZone15 | 3.8056239 | 0.7392212 | 5.1481532 | 0.0000003 |
| StatZone16 | 3.8604000 | 0.7354000 | 5.2493880 | 0.0000002 |
| StatZone17 | 3.6891521 | 0.7337145 | 5.0280485 | 0.0000005 |
| StatZone18 | 3.6498597 | 0.7348862 | 4.9665649 | 0.0000007 |
| StatZone19 | 3.8149649 | 0.7385118 | 5.1657465 | 0.0000002 |
| StatZone20 | 3.1150148 | 0.7417669 | 4.1994527 | 0.0000268 |
| StatZone21 | 2.9070554 | 0.7492185 | 3.8801169 | 0.0001044 |
I’ve seen worse, obviously not perfect though.
Generate the data for a timeline using estimated marginal means. Then plot that timeline with Confidence interval.
Model prediction (black) & observed mean values (blue), both standardized by overall mean
State landings data taken from GEDAR assesment and covers years 2000-2011. The values were divided by the overall mean for the timeseries for each state then plotted. They aren’t weighted according to which State catches more so this plot isn’t great.
Pretty noisy, and gives you a sense that either the landings reflect the population poorly, or that the population is resilient to some.
Statzones start counting at 1 (FL Keyes) and continue through brownsville Texas (22), 12 is around the Chandeleurs, and 13 is Terrebonne Bay and the MS Birdfoot.
Update 10/3/2019
Need To account for catch rates not being discrete with either an offset or with weights
Formula for using an offset:
Log(Catch)y, s, r ∼ λ + λyYear + λsSeason+ λrRegion + Log(Towtime)
Link to Reference: https://stats.stackexchange.com/questions/66791/where-does-the-offset-go-in-poisson-negative-binomial-regression/66878#66878
Link to formula for GLM https://newonlinecourses.science.psu.edu/stat504/node/216/
Use emmeans on year + season + region
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -3.9233196 | 0.1294643 | -30.3042577 | 0.0000000 |
| Year_f2010 | 0.2493999 | 0.1683396 | 1.4815290 | 0.1384657 |
| Year_f2011 | 0.6976250 | 0.1693122 | 4.1203466 | 0.0000378 |
| Year_f2012 | -0.5560169 | 0.1971552 | -2.8201997 | 0.0047994 |
| Year_f2013 | -0.5026238 | 0.2078878 | -2.4177655 | 0.0156161 |
| Year_f2014 | 0.4309158 | 0.1736025 | 2.4821987 | 0.0130574 |
| Year_f2015 | 0.6461253 | 0.1691059 | 3.8208326 | 0.0001330 |
| Year_f2016 | 0.0605462 | 0.1843363 | 0.3284552 | 0.7425675 |
| Year_f2017 | 0.2844148 | 0.1790591 | 1.5883852 | 0.1121993 |
| Year_f2018 | 0.0478424 | 0.2244907 | 0.2131152 | 0.8312371 |
| SeasonFall | -1.2988110 | 0.0956061 | -13.5850246 | 0.0000000 |
| regionLouisiana | 0.4071318 | 0.1023161 | 3.9791566 | 0.0000692 |
| regionMS Bight | -0.0857449 | 0.1501854 | -0.5709272 | 0.5680490 |
| regionFlorida | -2.8634887 | 0.1517673 | -18.8676315 | 0.0000000 |
| Year_f | Season | region | response | SE | df | asymp.LCL | asymp.UCL |
|---|---|---|---|---|---|---|---|
| 2009 | Summer | Texas | 0.5915148 | 0.0765800 | Inf | 0.4589500 | 0.7623700 |
| 2010 | Summer | Texas | 0.7590644 | 0.1115539 | Inf | 0.5690941 | 1.0124490 |
| 2011 | Summer | Texas | 1.1883387 | 0.1766205 | Inf | 0.8880304 | 1.5902034 |
| 2012 | Summer | Texas | 0.3392271 | 0.0602380 | Inf | 0.2395185 | 0.4804431 |
| 2013 | Summer | Texas | 0.3578317 | 0.0677764 | Inf | 0.2468619 | 0.5186849 |
| 2014 | Summer | Texas | 0.9101437 | 0.1408446 | Inf | 0.6720268 | 1.2326316 |
| 2015 | Summer | Texas | 1.1286889 | 0.1684645 | Inf | 0.8424161 | 1.5122440 |
| 2016 | Summer | Texas | 0.6284352 | 0.1035753 | Inf | 0.4549567 | 0.8680623 |
| 2017 | Summer | Texas | 0.7861137 | 0.1252708 | Inf | 0.5752313 | 1.0743065 |
| 2018 | Summer | Texas | 0.6205021 | 0.1286445 | Inf | 0.4133038 | 0.9315735 |
| 2009 | Fall | Texas | 0.1613984 | 0.0225483 | Inf | 0.1227386 | 0.2122351 |
| 2010 | Fall | Texas | 0.2071153 | 0.0320308 | Inf | 0.1529580 | 0.2804479 |
| 2011 | Fall | Texas | 0.3242454 | 0.0499073 | Inf | 0.2398050 | 0.4384190 |
| 2012 | Fall | Texas | 0.0925602 | 0.0174351 | Inf | 0.0639863 | 0.1338939 |
| 2013 | Fall | Texas | 0.0976365 | 0.0195894 | Inf | 0.0658917 | 0.1446752 |
| 2014 | Fall | Texas | 0.2483382 | 0.0398646 | Inf | 0.1813026 | 0.3401599 |
| 2015 | Fall | Texas | 0.3079696 | 0.0479826 | Inf | 0.2269279 | 0.4179532 |
| 2016 | Fall | Texas | 0.1714723 | 0.0301160 | Inf | 0.1215336 | 0.2419312 |
| 2017 | Fall | Texas | 0.2144959 | 0.0356782 | Inf | 0.1548225 | 0.2971692 |
| 2018 | Fall | Texas | 0.1693078 | 0.0376230 | Inf | 0.1095281 | 0.2617146 |
| 2009 | Summer | Louisiana | 0.8887522 | 0.1112690 | Inf | 0.6953652 | 1.1359218 |
| 2010 | Summer | Louisiana | 1.1404959 | 0.1634662 | Inf | 0.8611757 | 1.5104128 |
| 2011 | Summer | Louisiana | 1.7854816 | 0.2596853 | Inf | 1.3426237 | 2.3744140 |
| 2012 | Summer | Louisiana | 0.5096894 | 0.0886677 | Inf | 0.3624324 | 0.7167772 |
| 2013 | Summer | Louisiana | 0.5376429 | 0.0998734 | Inf | 0.3735712 | 0.7737745 |
| 2014 | Summer | Louisiana | 1.3674928 | 0.2051141 | Inf | 1.0191801 | 1.8348441 |
| 2015 | Summer | Louisiana | 1.6958575 | 0.2465628 | Inf | 1.2753581 | 2.2550001 |
| 2016 | Summer | Louisiana | 0.9442252 | 0.1499758 | Inf | 0.6916321 | 1.2890685 |
| 2017 | Summer | Louisiana | 1.1811375 | 0.1848247 | Inf | 0.8691690 | 1.6050800 |
| 2018 | Summer | Louisiana | 0.9323058 | 0.1892990 | Inf | 0.6262202 | 1.3880007 |
| 2009 | Fall | Louisiana | 0.2425014 | 0.0325774 | Inf | 0.1863651 | 0.3155469 |
| 2010 | Fall | Louisiana | 0.3111912 | 0.0466564 | Inf | 0.2319573 | 0.4174904 |
| 2011 | Fall | Louisiana | 0.4871794 | 0.0728657 | Inf | 0.3633941 | 0.6531306 |
| 2012 | Fall | Louisiana | 0.1390718 | 0.0255788 | Inf | 0.0969800 | 0.1994327 |
| 2013 | Fall | Louisiana | 0.1466991 | 0.0287854 | Inf | 0.0998629 | 0.2155018 |
| 2014 | Fall | Louisiana | 0.3731287 | 0.0577261 | Inf | 0.2755311 | 0.5052969 |
| 2015 | Fall | Louisiana | 0.4627250 | 0.0697957 | Inf | 0.3442951 | 0.6218920 |
| 2016 | Fall | Louisiana | 0.2576375 | 0.0435148 | Inf | 0.1850298 | 0.3587374 |
| 2017 | Fall | Louisiana | 0.3222805 | 0.0523402 | Inf | 0.2344197 | 0.4430717 |
| 2018 | Fall | Louisiana | 0.2543853 | 0.0552901 | Inf | 0.1661442 | 0.3894922 |
| 2009 | Summer | MS Bight | 0.5429090 | 0.0862470 | Inf | 0.3976531 | 0.7412243 |
| 2010 | Summer | MS Bight | 0.6966908 | 0.1262825 | Inf | 0.4883722 | 0.9938691 |
| 2011 | Summer | MS Bight | 1.0906909 | 0.2001007 | Inf | 0.7612693 | 1.5626622 |
| 2012 | Summer | MS Bight | 0.3113522 | 0.0647840 | Inf | 0.2070808 | 0.4681274 |
| 2013 | Summer | MS Bight | 0.3284281 | 0.0712170 | Inf | 0.2147158 | 0.5023617 |
| 2014 | Summer | MS Bight | 0.8353556 | 0.1559391 | Inf | 0.5793944 | 1.2043937 |
| 2015 | Summer | MS Bight | 1.0359426 | 0.1880720 | Inf | 0.7257764 | 1.4786608 |
| 2016 | Summer | MS Bight | 0.5767956 | 0.1130683 | Inf | 0.3927916 | 0.8469965 |
| 2017 | Summer | MS Bight | 0.7215174 | 0.1373168 | Inf | 0.4968771 | 1.0477185 |
| 2018 | Summer | MS Bight | 0.5695144 | 0.1308793 | Inf | 0.3629872 | 0.8935484 |
| 2009 | Fall | MS Bight | 0.1481360 | 0.0248930 | Inf | 0.1065671 | 0.2059198 |
| 2010 | Fall | MS Bight | 0.1900963 | 0.0357761 | Inf | 0.1314552 | 0.2748967 |
| 2011 | Fall | MS Bight | 0.2976016 | 0.0560752 | Inf | 0.2057069 | 0.4305482 |
| 2012 | Fall | MS Bight | 0.0849543 | 0.0185130 | Inf | 0.0554235 | 0.1302197 |
| 2013 | Fall | MS Bight | 0.0896136 | 0.0203650 | Inf | 0.0574031 | 0.1398983 |
| 2014 | Fall | MS Bight | 0.2279318 | 0.0437917 | Inf | 0.1564108 | 0.3321568 |
| 2015 | Fall | MS Bight | 0.2826632 | 0.0530329 | Inf | 0.1956897 | 0.4082916 |
| 2016 | Fall | MS Bight | 0.1573822 | 0.0323895 | Inf | 0.1051422 | 0.2355776 |
| 2017 | Fall | MS Bight | 0.1968704 | 0.0387482 | Inf | 0.1338585 | 0.2895441 |
| 2018 | Fall | MS Bight | 0.1553954 | 0.0378903 | Inf | 0.0963582 | 0.2506040 |
| 2009 | Summer | Florida | 0.0337573 | 0.0057339 | Inf | 0.0241984 | 0.0470923 |
| 2010 | Summer | Florida | 0.0433193 | 0.0077698 | Inf | 0.0304796 | 0.0615678 |
| 2011 | Summer | Florida | 0.0678177 | 0.0121820 | Inf | 0.0476917 | 0.0964368 |
| 2012 | Summer | Florida | 0.0193595 | 0.0039983 | Inf | 0.0129150 | 0.0290195 |
| 2013 | Summer | Florida | 0.0204212 | 0.0044363 | Inf | 0.0133403 | 0.0312607 |
| 2014 | Summer | Florida | 0.0519413 | 0.0092936 | Inf | 0.0365773 | 0.0737588 |
| 2015 | Summer | Florida | 0.0644135 | 0.0111488 | Inf | 0.0458828 | 0.0904282 |
| 2016 | Summer | Florida | 0.0358644 | 0.0069272 | Inf | 0.0245614 | 0.0523688 |
| 2017 | Summer | Florida | 0.0448630 | 0.0084294 | Inf | 0.0310423 | 0.0648370 |
| 2018 | Summer | Florida | 0.0354116 | 0.0079605 | Inf | 0.0227927 | 0.0550169 |
| 2009 | Fall | Florida | 0.0092109 | 0.0017203 | Inf | 0.0063874 | 0.0132825 |
| 2010 | Fall | Florida | 0.0118199 | 0.0022970 | Inf | 0.0080761 | 0.0172993 |
| 2011 | Fall | Florida | 0.0185045 | 0.0035662 | Inf | 0.0126833 | 0.0269973 |
| 2012 | Fall | Florida | 0.0052823 | 0.0011797 | Inf | 0.0034098 | 0.0081833 |
| 2013 | Fall | Florida | 0.0055721 | 0.0013050 | Inf | 0.0035210 | 0.0088179 |
| 2014 | Fall | Florida | 0.0141725 | 0.0027302 | Inf | 0.0097157 | 0.0206738 |
| 2015 | Fall | Florida | 0.0175756 | 0.0032988 | Inf | 0.0121660 | 0.0253907 |
| 2016 | Fall | Florida | 0.0097858 | 0.0020588 | Inf | 0.0064791 | 0.0147802 |
| 2017 | Fall | Florida | 0.0122411 | 0.0024742 | Inf | 0.0082372 | 0.0181913 |
| 2018 | Fall | Florida | 0.0096623 | 0.0023708 | Inf | 0.0059734 | 0.0156293 |
These maps are made using only the model data so years 2010-2018 and only the stat zones with enough samples and some catch. The gaps around Pensacola and Tampa are those areas I took out because there was no catch and they made the models confidence intervals absurd.
Comments
I have polygons for the stat zones and was thinking I would make a map with those polygons shaded by predicted catch or with the mean catch visible within them. That file is too big for my laptop to process though so I couldn’t add it here. There’s really three big things with the data.
If the goal was to explain why this is so I’d say that its likely the difference in bottom types in these two offshore areas sand/hard bottom vs. mud and then coupled with more crabs inshore off Louisiana so you are just starting with more crabs to begin with. But, for this paper I’m thinking we point out